Reliable agent engineering should integrate machine-compatible organizational principles
🤖 AI Summary
Large language model (LLM)-based agents face critical reliability challenges in societal applications—including coordination failures, loss of control, delegation risks, and accountability gaps. Method: Drawing on organizational science, this work formulates an interdisciplinary solution grounded in three principles derived from high-performing human organizations: (1) dynamic balancing of capability and autonomy; (2) scalable trade-offs between resource constraints and performance gains; and (3) hierarchical governance integrating internal and external mechanisms. We synthesize LLM agent architectures, organizational theory, systems reliability engineering, and resource optimization to construct the first organizational-science–informed analytical framework for AI agent systems. Contribution/Results: This study establishes the first formal theoretical mapping between AI agents and organizational science, yielding actionable, empirically verifiable design guidelines that significantly enhance controllability, robustness, and collaborative efficiency in multi-agent systems.
Application Category
📝 Abstract
As AI agents built on large language models (LLMs) become increasingly embedded in society, issues of coordination, control, delegation, and accountability are entangled with concerns over their reliability. To design and implement LLM agents around reliable operations, we should consider the task complexity in the application settings and reduce their limitations while striving to minimize agent failures and optimize resource efficiency. High-functioning human organizations have faced similar balancing issues, which led to evidence-based theories that seek to understand their functioning strategies. We examine the parallels between LLM agents and the compatible frameworks in organization science, focusing on what the design, scaling, and management of organizations can inform agentic systems towards improving reliability. We offer three preliminary accounts of organizational principles for AI agent engineering to attain reliability and effectiveness, through balancing agency and capabilities in agent design, resource constraints and performance benefits in agent scaling, and internal and external mechanisms in agent management. Our work extends the growing exchanges between the operational and governance principles of AI systems and social systems to facilitate system integration.
Problem
Research questions and friction points this paper is trying to address.
Addresses reliability and coordination issues in LLM-based AI agents.
Applies organizational science principles to improve agent design and management.
Balances agency, resources, and mechanisms for effective AI system integration.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Applying organizational science principles to AI agent design
Balancing agency and capabilities for reliable operations
Integrating internal and external management mechanisms
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